Tweeting Educational Technology: A Tale of Professional Community of Practice

Article excerpt

ABSTRACT

This paper explores an Israeli professional community on Twitter practicing educational technology. Networking analysis of 42 users and 296 structural connections among them revealed that the adoption of Twitter was normally distributed and active participation was asymmetrical - 14.3% of users produced 80% of the tweets. Investment in participation was highly gratified by influence on the audience.

Keywords: Professional Communities of Practice, Twitter, Social Network Analysis, Participation, Diffusion of Innovations, Uses and Gratification

INTRODUCTION

Professional communities of practice have moved recently from online forums to social network platforms. The Diffusion of Innovation Theory (Rogers, 2003) suggests that adoption of an innovation over time is normally distributed: from innovators (2.5%) and early adopters (13.5%), through early (34%) and late majority (34%), to laggards (16%). This approach offers valuable insights for modeling the entire life cycle of innovation adoptions (Chang, 2010).

This paper investigates social networking behavior on an Israeli professional community of people working or studying the field of educational / information technologies and connected by Twitter. The stream of messages on Twitter allows community members to be peripherally aware of surrounding conversations and to consume information without active participation (Boyd, Golder, & Lotan, 2010). Therefore, compared to a "long tail" distribution of active participation in other social media (Blau, 2011) ranging near the 20:80 rule, according to which 20% of the participants produce 80% of the content, tweeting might be even more unequal. Structural connections between Twitter users are directed; participants can "follow" other users without being reciprocated (Boyd et al., 2010). These connections enable the exploration of behavior on Twitter in terms of user investment in participation by tweeting and following others, as well as gratification mechanisms - different forms of influencing the audience.

METHODOLOGY

The activities of 42 users and 296 structural edges (Leavitt, Burchard, Fisher, & Gilbert, 2009) among them during the period of 4.5 years (March 2007 - October 2011) were extracted, analyzed, and visualized using NodeXL, an open-source application for network analysis. The participants were located using relevant search queries in Hebrew and English, searching for Twitter accounts of known researchers and professionals in the field and checking followers of relevant academic institutions and professional organizations (Forkosh-Baruch & Hershkovitz, 2012). Only Twitter accounts with a minimal level of activity (at least 5 tweets or favoriting of tweets) during the three months before extraction of the data were included in this study (Cha, Haddadi, Benevenuto, & Gummadi, 2010). The data was analyzed in terms of user investment into the community (e.g., active participation by tweeting and passive participation by following others) and gratifications (e.g., influence on the audience, measured by 1- the degree of centrality into the network measured by the PageRank (Weng, Lim, Jiang, & He, 2010) - an analysis algorithm, named after Larry Page that assigns a numerical weighting to each element of a hyperlinked set of documents in order to assess its relative importance within the set, 2-number of followers (Cha et al., 2010), and 3number/percentage of tweets marked as favorites).

FINDINGS

The evolution of the community during the period of investigation as analyzed through dynamic filters of NodeXL was consistent with the normal distribution curve (Figures 1-4), as proposed by Diffusion of Innovations Theory (Rogers, 2003). …